Fetching the paper…
Reading the bibliography…
Preference-based reinforcement learning (PbRL) has shown impressive capabilities in training agents without reward engineering.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
Earlier work this paper cites.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E. Terry · 1952
Earlier work this paper cites.
Convergence of stochastic iterative dynamic programming algorithms
Tommi Jaakkola, Michael Jordan, and Satinder Singh · 1993
Earlier work this paper cites.
Convergence of q-learning: A simple proof
Francisco S Melo · 2001
Earlier work this paper cites.
Nearest neighbor estimates of entropy
Neeraj Misra, Harshinder Singh, and Vladimir Hnizdo · 2003
Earlier work this paper cites.
Reinforcement learning by reward-weighted regression for operational space control
Jan Peters and Stefan Schaal · 2007
Earlier work this paper cites.
robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, and Roberto Martín-Martín · 2009
Earlier work this paper cites.
Algorithms for reinforcement learning
Csaba Szepesvári · 2010
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
Earlier work this paper cites.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
Earlier work this paper cites.
Visual closed-loop control for pouring liquids
Connor Schenck and Dieter Fox · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Collective robot reinforcement learning with distributed asynchronous guided policy search
Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, and Sergey Levine · 2017
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Earlier work this paper cites.
Reward learning from human preferences and demonstrations in atari
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, and Dario Amodei · 2018
Earlier work this paper cites.
gym-sokoban
Max-Philipp B. Schrader · 2018
Earlier work this paper cites.
Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
Earlier work this paper cites.
Exponentially weighted imitation learning for batched historical data
Qing Wang, Jiechao Xiong, Lei Han, peng sun, Han Liu, and Tong Zhang · 2018
Cited alongside, same era.
Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Christopher Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, and Susan Zhang · 2019
Cited alongside, same era.
Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
Cited alongside, same era.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
Cited alongside, same era.
Sample-efficient deep reinforcement learning via episodic backward update
Su Young Lee, Choi Sungik, and Sae-Young Chung · 2019
Recursively summarizing books with human feedback
Jeff Wu, Long Ouyang, Daniel M. Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, and Paul F. Christiano · 2021
Later among the works it cites.
Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning
Joseph Early, Tom Bewley, Christine Evers, and Sarvapali Ramchurn · 2022
Later among the works it cites.
Topological experience replay
Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, and Pulkit Agrawal · 2022
Later among the works it cites.
Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2022
Later among the works it cites.
Reward uncertainty for exploration in preference-based reinforcement learning
Xinran Liang, Katherine Shu, Kimin Lee, and Pieter Abbeel · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Reverse experience replay
Egor Rotinov · 2019
Cited alongside, same era.
Grandmaster level in starcraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
Cited alongside, same era.
D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
Cited alongside, same era.
Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
Cited alongside, same era.
Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning
Runze Liu, Fengshuo Bai, Yali Du, and Yaodong Yang · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe · 2022
Later among the works it cites.
SURF: Semi-supervised reward learning with data augmentation for feedback-efficient preference-based reinforcement learning
Jongjin Park, Younggyo Seo, Jinwoo Shin, Honglak Lee, Pieter Abbeel, and Kimin Lee · 2022
Later among the works it cites.
Defining and characterizing reward gaming
Joar Skalse, Nikolaus Howe, Dmitrii Krasheninnikov, and David Krueger · 2022
Later among the works it cites.
Skill preferences: Learning to extract and execute robotic skills from human feedback
Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi, Pieter Abbeel, and Michael Laskin · 2022
Later among the works it cites.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
Later among the works it cites.
Extreme q-learning: Maxent RL without entropy
Divyansh Garg, Joey Hejna, Matthieu Geist, and Stefano Ermon · 2023
Later among the works it cites.
Inverse preference learning: Preference-based RL without a reward function
Joey Hejna and Dorsa Sadigh · 2023
Later among the works it cites.
Few-shot preference learning for human-in-the-loop RL
Donald Joseph Hejna III and Dorsa Sadigh · 2023
Later among the works it cites.
Beyond reward: Offline preference-guided policy optimization
Yachen Kang, Diyuan Shi, Jinxin Liu, Li He, and Donglin Wang · 2023
Later among the works it cites.
Preference transformer: Modeling human preferences using transformers for RL
Changyeon Kim, Jongjin Park, Jinwoo Shin, Honglak Lee, Pieter Abbeel, and Kimin Lee · 2023
Later among the works it cites.
Drl-based trajectory tracking for motion-related modules in autonomous driving
Yinda Xu and Lidong Yu · 2023
Later among the works it cites.
Craftenv: A flexible collective robotic construction environment for multi-agent reinforcement learning
Rui Zhao, Xu Liu, Yizheng Zhang, Minghao Li, Cheng Zhou, Shuai Li, and Lei Han · 2023
Later among the works it cites.